基于OCO-2卫星观测的浙江省典型城市CO2柱浓度变化特征及碳排放重点源识别应用

为有效制定城市层面的低碳发展政策,实现碳达峰的发展目标,利用碳卫星2号（OCO-2）监测的高分辨率大气CO₂柱浓度数据（XCO₂）,分析浙江省杭州、宁波和嘉兴3个典型城市的XCO₂变化特征,以及人类活动和XCO₂变化的关系;识别城市碳排放热点区域,评估碳排放热点源对XCO₂的影响,并利用拉格朗日粒子扩散模型（LPDM）进行验证。结果表明：（1）2016—2021年3个城市的XCO₂年增长量分别为3.1×10^-6,2.3×10^-6和2.2×10^-6,杭州的增长量最为明显;杭州和宁波在2019—2021年XCO₂增量明显,分别为8.0×10^-6和5.7×10^-6。杭州XCO₂的变化趋势与临安大气本底站CO₂观测数据的变化趋势一致。（2）与2017年相比,3个城市的建筑用地面积都略有增加,分别增加了0.9%,2.2%和4.8%;从人口和GDP数据来看,2016—2021年3个城市也均呈持续增加的变化趋势。表明CO₂浓度升高与人类活动密切相关。（3）XCO₂正距平高值区域基本都对应了碳排放热点源（电力企业）的下风向地区,电力企业CO₂的排放会导致下风向地区的XCO₂出现局地性增长,增量为7×10^-6～9×10^-6。     

遂宁市夏季大气挥发性有机物污染特征及来源解析

2019年7-8月在四川省遂宁市实验学校、遂宁中学、金鱼小学、石溪浩4个点位同步开展为期20d的挥发性有机物(VOCs)离线观测,分析了遂宁市VOCs浓度时空分布特征、臭氧生成潜势(OFP)和VOCs主要来源。遂宁市TVOC体积浓度为39.4×10^-9,占比较高的组分为OVOCs和烷烃,体积浓度分别为15.6×10^-9和13.3×10^-9,占比分别为39.5%和33.6%。遂宁中学、金鱼小学、石溪浩24 h平均体积浓度分别为29.8 ×10^-9、58.4 ×10^-9、30.0×10^-9;加密点实验学校的小时平均浓度为22.9×10^-9。遂宁市总OFP为166.7 μg/m³,占比最大的为烯烃(33.1%)。实验学校、遂宁中学、金鱼小学、石溪浩OFP浓度分别为101.2、134.4、243.6、122.1 μg/m³。金鱼小学采样点位于工业园区下风向,受工业园区企业排放源影响,VOCs浓度和OFP值均明显高于其他点位。PMF模型源解析结果表明:遂宁市VOCs来源占比最大的为工业排放源,达32%;其次为机动车尾气源、燃烧源,占比均达17%;油气挥发源、天然源、溶剂使用源分别占13%、11%、10%。工业源、机动车尾气来源占比最高的均是金鱼小学,分别为39%、30%;天然源占比较高的是实验学校(13%)和石溪浩(10%)。     

工业园区和城区VOCs组成及化学活性的空间差异对清远大气臭氧污染分布的影响

工业化与城镇化交替演进使珠三角及其周边地区土地利用类型较为复杂。快速的城市化进程使城市建成区与大量村镇工业园区互相交错。这种变化势必会增加挥发性有机物(VOCs)在组分构成和空间分布上的复杂性,并对臭氧(O₃)污染的时空变化产生影响。为厘清这种排放的空间异质性特征及其对O₃污染分布的影响,分别选取可以代表清远市典型工业园区和城市建成区的站点开展观测研究。结果表明:工业园区和城市建成区VOCs浓度水平和污染特征有较大的空间差异,其中代表村镇工业园区的龙塘站VOCs日均浓度为30.42×10^-9,高于代表城市建成区的技师学院站(17.32×10^-9)。龙塘站二甲苯和甲苯的臭氧生成潜势(OFP)比技师学院站高57.6×10^-9,且该值相当于技师学院站排名前10位物种OFP的总和。气象分析表明:2个站点之间并非彼此的上、下风向,而是共同受到局地气团的影响。源解析结果表明:源排放是造成这种空间异质性的内因,其中交通源对技师学院的贡献更高,而工业相关排放源对龙塘的贡献更高。该研究进一步比较了周边站点O₃时间序列的一致性,并模拟2个站点的O₃生成速率。研究发现O₃在局地范围内变化较小,高VOCs排放的地点对局地O₃有较高的贡献,局地内不同地点的O₃生成过程也存在较大差异。据此,笔者提出O₃污染防控建议:短期内可通过技术手段和观测数据发现O₃污染的重要贡献点,并进行针对性的"散乱污"清理整治和涉VOCs行业综合整治,长期看应科学合理规划城市发展布局和产业布局,预留城市通风廊道,以有效减少O₃污染。     

威海市秋季VOCs污染特征及来源解析

为探究威海市秋季挥发性有机物（VOCs）污染特征及来源,于2021年9月10—20日采用手工加密监测法对威海市秋季大气中VOCs进行监测,分析了气象因素对臭氧（O₃）及其前体物的影响和VOCs污染特征,并利用正交矩阵因子模型（PMF）方法对VOCs来源进行了研究。结果表明,威海市温度对O₃生成影响明显,尤其是高温、低湿、扩散较差气象条件下,有利于O₃前体物的反应消耗,促使O₃生成及累积。观测期间,威海市秋季φ（VOCs）平均值为47.84×10^-9,VOCs中体积分数占比最高的为含氧挥发性有机物（OVOCs）,占比为58.0%,其次为烷烃（21.6%）、卤代烃（10.2%）。O₃生成潜势（OFP）平均值为393.95μg/m³,对OFP的贡献占比最高的为OVOCs（74.1%）,其次为芳香烃（12.6%）、烷烃（7.0%）和烯烃（5.4%）。PMF源解析结果显示,机动车尾气排放源、工艺过程源、船舶尾气排放源和溶剂使用源是威海市秋季VOCs排放主要来源,贡献占比分别为30.4%,23.9%,21.1%,16.5%。控制机动车排放和工艺过程排放是控制威海市秋季VOCs污染的重要途径。     

北京市大气气态汞变化特征

采用Tekran 2537X大气汞分析仪在线测量北京市城区大气中气态元素汞(GEM,简称大气汞) 浓度,研究大气汞浓度随不同气象条件的变化特征。通过分析2016年10月—2017年9月大气汞监测数据发现,该监测点全年大气汞浓度为0.48～16.25 ng/m³,均值为(3.41±1.79)ng/m³。春季、夏季、秋季和冬季大气汞浓度均值依次为2.93 、2.95、4.27、3.37 ng/m³,其中,秋季大气汞浓度明显高于其他季节 。秋季大气汞浓度显著偏高可能由不利的大气扩散条件导致。大气汞夜间浓度显著高于白天浓度。同时,将大气汞与SO₂、CO及PM_2.5进行相关性分析,发现大气汞浓度变化趋势与SO₂、CO和PM_2.5呈显著正相关。结合风向和风速进行污染来源分析,得到该点位大气汞在西南和东北方向上受人为排放源影响较大。污染源类型分析表明,冬季大气汞与CO同源性强,主要来自本地供暖用煤。     

南京某典型化工园区春季VOCs污染特征和臭氧生成潜势分析

利用2020年3月28日—5月3日南京某典型化工园区挥发性有机物（VOCs）离线监测数据,分析了园区内VOCs污染特征及臭氧生成潜势（OFP）。结果表明,春季园区φ（VOCs）范围为22.3×10^-9 ~892.6×10^-9,82.1%频率的φ（VOCs）<100×10^-9;VOCs组分占比表现为：烷烃＞含氧挥发性有机物（OVOCs）＞烯烃＞卤代烃>芳香烃>炔烃>有机硫。高体积分数VOCs中烷烃和烯烃占比高于低体积分数VOCs,受园区内部储罐存储、运输、转运等过程产生的油气挥发及石油化工原料、合成材料的生产影响显著。不同时刻φ（VOCs）表现为夜间最高、早晨其次、下午最低的变化特征,这与园区内部VOCs排放累积、大气边界层抬升和大气光化学反应等因素有关。OFP值范围为166.2~6 920.9 ,μg/m³,56.0%频率的OFP<500。     

某典型交通路口大气颗粒物(PM10和PM2.5)中多氯联苯季节变化特征

应用同位素稀释高分辨率气相色谱-高分辨质谱 (HRGC-HRMS) 联用技术对北京市北四环典型交通路口大气颗粒物PM₁₀和PM_2.5中多氯联苯(PCBs)进行了监测,分析了PCBs浓度水平、单体组成特征、粒径分布规律和季节变化趋势。结果表明:大气颗粒物PM₁₀和PM_2.5样品中19种PCBs浓度和毒性浓度(TEQ,以世界卫生组织毒性当量因子WHO-TEF计)分别为1.05～13.83 pg/m³(平均值为6.66 pg/m³)和1.24～15.18 fg/m³ (平均值为6.84 fg/m³)、0.80～8.51 pg/m³(平均值为4.32 pg/m³)和0.88～13.40 fg/m³ (平均值为5.90 fg/m³),PM₁₀和PM_2.5中PCBs的单体分布模式相似,浓度丰度最大的是PCB-28和PCB-209,而对毒性当量贡献最大的是PCB-126。PCBs浓度季节变化明显,冬、春季明显高于夏、秋季。 PCBs浓度季节变化特征表明,不同季节采样点PCBs来源不同,除历史使用外,采暖季节可能主要来自机动车排放和化石燃料的燃烧,而非采暖季节主要来自机动车排放。粒径分布表现为PCBs倾向于富集在PM_2.5中,占PM₁₀总浓度的61%～87%(平均值为72%)。     

济南市大气细颗粒物中二(口恶)英污染水平及季节变化

2021年对济南市大气PM_2.5中17种2,3,7,8氯取代二(口恶)英(PCDD/Fs)污染现状进行监测。对其异构体分布、指示性单体、季节变化规律等特征及其与常规污染物相关性进行了分析。结果表明:大气PM_2.5中PCDD/Fs浓度范围和年平均值分别为0.157~1.595 pg/m³和0.785 pg/m³,而毒性当量(以I-TEQ计)范围和年平均值分别为0.009~0.116 pg TEQ/m³和0.052 pg TEQ/m³。PCDD/Fs浓度与毒性当量季节变化特征显著,均呈现出冬季>春季>秋季>夏季的情况,可能由季节性排放源和气象条件不同导致。不同季节PCDD/Fs异构体分布模式一致,主要由高氯代(1,2,3,4,6,7,8-HpCDF、OCDD、OCDF和1,2,3,4,6,7,8-HpCDD)单体组成;而对毒性当量贡献最大的单体是2,3,4,7,8-PeCDF,其与总毒性当量具有较好的相关性。同时,PCDD/Fs浓度与SO₂、NO₂、PM_2.5等大气常规污染物呈显著正相关。这表明,大气PM_2.5中PCDD/Fs与常规污染物的生成和排放密切相关。     

基于生命周期评价法的条斑紫菜养殖加工产业碳足迹分析

应用生命周期评价法（LCA）对条斑紫菜养殖加工行业进行了全周期的碳足迹分析，明确了各环节中碳排放源的种类和数量。结果表明，100亩条斑紫菜养殖加工过程中碳排放总量为1.25×10⁵~2.47×10⁵ kg CO₂，远高于条斑紫菜100亩养殖形成的可移出碳汇量（9.43×10³ kg CO₂）。基于全产业链的分析，条斑紫菜产业尚不是一个碳汇产业。养殖阶段碳排放量最大，排放源主要来自石油化工材料的大量使用。二次加工阶段碳排放量仅次于养殖阶段，排放源主要来自纸壳包装和塑料包装的大量使用。在一次加工阶段，热源的使用是影响该阶段碳排放的主要影响因素，生物质燃料是碳排放量最低的热源形式。     

1997—2010年北京市大气降水离子特征变化趋势研究

依据北京市环境保护监测中心1997—2010年降水监测资料,分析北京地区降水中离子特征及变化趋势,阐明北京市降水污染现状及变化特征。结合北京市特有的气象条件、地形地貌和工业分布情况,分析污染物来源及污染变化趋势。研究表明:年度降水电导率呈现波动变化,降水污染严重程度依次为南部郊区>市区>北部背景点。北京地区大气降水中的主要阳离子成分是Ca²⁺和NH₄⁺,主要阴离子成分是SO₄^2-和NO₃^-。近年来[SO₄^2-]/[NO₃^-]比值逐步下降,污染类型由典型硫酸型发展为硫酸+硝酸混合型。阳离子[Ca²⁺]/[NH₄⁺]比值下降,碱性离子缓冲能力降低。9种离子各季节浓度变化趋势基本一致,由高到低依次是春季>秋季>冬季>夏季,这种季节变化特征与气象因素密切相关。相关性及聚类分析表明:NO₃^-与SO₄^2-存在很强相关性,说明其前体物SO₂和NO_X在大气中经常一同排放且进入降水途径相同;H⁺浓度不是由某个离子决定,是所有致酸离子和中和离子相互作用的结果,而NH₄⁺来源不同于其它离子,北京地区的氨存在其单独排放源。     

Seasonal Variations of PM10 and TSP in Residential and Industrial Sites in an Urban Area of Kolkata, India

The objective of the study is to investigate seasonal and spatial variations of PM₁₀ (particulate matter with aerodynamic diameter less than or equal to 10 μm) and TSP (total suspended particulate matter) of an Indian Metropolis with high pollution and population density from November 2003 to November 2004. Ambient concentration measurements of PM₁₀ and TSP were carried out at two monitoring sites of an urban region of Kolkata. Monitoring sites have been selected based on the dominant activities of the area. Meteorological parameters such as wind speed, wind direction, rainfall, temperature and relative humidity were also collected simultaneously during the sampling period from Indian Meteorological Department, Kolkata. The 24 h average concentrations of PM₁₀ and TSP were found in the range 68.2–280.6 μg/m³ and 139.3–580.3 μg/m³ for residential (Kasba) area, while 62.4–401.2 μg/m³ and 125.7–732.1 μg/m³ for industrial (Cossipore) area, respectively. Winter concentrations of particulate pollutants were higher than other seasons, irrespective of the monitoring sites. It indicates a longer residence time of particulates in the atmosphere during winter due to low winds and low mixing height. Spread of air pollution sources and non-uniform mixing conditions in an urban area often result in spatial variation of pollutant concentrations. The higher particulate pollution at industrial area may be attributed due to resuspension of road dust, soil dust, automobile traffic and nearby industrial emissions. Particle size analysis result shows that PM₁₀ is about 52% of TSP at residential area and 54% at industrial area.     

Sampling The Soil In Long-Term Forest Plots: The Implications Of Spatial Variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.     

Assessing Spatial Occurrence of Ground Level Ozone around Coal Mining Areas of Chandrapur District, Maharashtra, India

Stratospheric input and photochemical ozone formation in the troposphere are the two main sources determining the ozone levels in the surface layer of the atmosphere. Because of the importance of ozone in controlling the atmospheric chemistry and its decisive role in the heat balance of atmosphere, leading to climate change, the examination of its formation and destruction are of great interest. This study characterized the distribution of Ground level Ozone (GLO) in Chandrapur district is lying between 19°25′N to 20°45′N and 78°50′E to 80°10′E. Continuous ozone analyzer was used to quantify GLO at thirteen locations fixed by Global Positioning System (GPS) during the winter of 2005–2006. The daily GLO at all the locations ranged between 6.4 and 24.8 ppbv with an average and standard deviation of 14.9 ± 6.5 ppbv. The maximum and minimum concentration occurs during 1300–1600 h and 0300–0500 h may be due to high solar radiation facilitating photochemical production of O₃ and downward mixing from the overlying air mass and in situ destruction of ozone by deposition and/or the reaction between O₃ and NO. GIS based spatial distribution of GLO in Chandrapur district is indicates that the central core of the district and southern sites experienced elevated levels of GLO relative to the northern and western areas. The sites near by Chandrapur city are particularly affected by elevated GLO. The average variation of GLO with temperature shows a significant correlation of r = 0.55 indicating a direct relationship between GLO and temperature. Similarly an attempt has been made to compare the GLO monitored data in Chandrapur district with the reported values for other locations in Indian cities. This generated database helps regulatory agencies to identify locations where the natural resources and human health could be at risk.     

沈阳臭氧污染时空分布特征及变化趋势

以沈阳2013—2015年臭氧(O_3)监测数据为基础,从地域差异及时间变化上分析了沈阳O_3浓度变化特征。结果表明:沈阳城市外围O_3浓度高于城市中心;O_3浓度变化具有明显季节特征,夏季O_3浓度最高,冬季最低;O_3浓度日变化呈单峰分布,谷值出现在06:00,峰值出现在14:00;O_3浓度出现明显"周末效应",周末白天O_3浓度高于工作日O_3浓度,夜间差异不大。     

郑州市 PM2．5和 PM10质量浓度变化特征分析

根据郑州市2013年PM2．5和PM10颗粒物连续自动监测数据,对郑州市各国控站点的PM2．5和PM10的达标情况、变化趋势等进行探讨分析。结果表明：2013年郑州市PM10和PM2．5的年均质量浓度均超过了新标准规定的年均值二级标准限值。 PM10和PM2．5月均值峰值出现在1月和10月,谷值出现在8月,各月PM2．5的超标天数都大于PM10。PM10和PM2．5冬季的日均值浓度明显高于其他季节,呈双峰型,夜晚浓度整体高于白天;PM2．5春、夏、秋三季日变化呈单峰型,PM10夏季和秋季呈单峰型,春季呈双峰型。 PM2．5和PM10日均值有着非常显著的线性相关关系,PM2．5和PM10浓度的比值（p）10月最高。     

Analysis of Wet Precipitation of Air Pollutants in Mumbai (India)

Bulk precipitation samples at Mumbai (India) were collectedduring the monsoon seasons of 1991 to 1996 and analysed forionic concentrations using an Ion Chromatograph DIONEX model100. The variability of sulphate to nitrate ratio in rainwaterfluctuates in a wide range from 1.5 to 20 and governed by thesulphate concentrations in the sample. The regression analysisof the data reveals that in the bulk precipitation at Mumbai, SO₄ ^2- is becoming increasingly important relative toNO₃ ^-. The role of meteorological influences onscavenging of air pollutants by rain water has been tried toexplain the phenomena. The computed wet deposition rates for Sand N during 1991–1996 show that the S deposition is higherthan N in all the years. There is a wide fluctuation indeposition rates of S ranging from 2 to 55 kg km² per annum.     

Measurement of Atmospheric Deposition Under Forest Canopies: Some Recommendations for Equipment and Sampling Design

Quantification of the forest water flux provides valuable information for the understanding of forest ecosystem functioning. As such, throughfall (and stemflow to a lesser extent) has been frequently measured. Although throughfall collection may seem relatively simple, the requirements to obtain reliable estimates are often underestimated. This review addresses the criteria to take into account when working out the sampling procedure, from the selection of equipment to implementation in the field. Sound sampling of the forest water flux is difficult due to its high spatial and temporal variation. The high costs entailed by the ideal sampling design often prohibit its implementation. Different procedures are available, some of which are compromises between the aim of the study (monitoring or experimental study, short or long term objectives, absolute or relative estimates, quality of the assessment to be achieved) and the available means.     

基于自动站资料的深圳灰霾特征对比分析

利用深圳自动气象站的气象要素和深圳大气成分监测系统采集的大气成分数据,分析了深圳城区和郊区灰霾季节变化、日变化差异和不同风向下污染物浓度差异,结果表明,城区由于人类活动频繁导致灰霾日比郊区多,以轻微灰霾偏多为主。秋、冬季城区冷空气活动频繁、能源消耗大,灰霾出现频率是郊区的1~2倍;春季冷空气和海上暖湿气流容易形成对峙,沿海颗粒物更容易吸湿增长,郊区灰霾频率反而比城区高25%;夏季对流强、降水频密,城郊差异最小。城区灰霾频率受早晚交通高峰期影响,日变化呈双峰型。而郊区受太阳辐射和光化学反应影响大,呈单峰型。偏北风条件下污染物浓度明显升高,偏南风带来的清洁空气使得颗粒物浓度降幅明显。     

2013—2019年青岛市空气质量变化特征分析

为掌握滨海城市环境空气质量变化特征,为污染精准管控和打赢蓝天保卫战提供科学参考,以沿海重要中心城市青岛市为研究区域,基于青岛市9个国控空气监测点位监测结果,对2013—2019年青岛市6项空气污染物浓度监测结果进行分析,总结归纳青岛市空气质量时间变化特征。结果表明：2019年,青岛市空气质量超标,超标指标为颗粒物;青岛市2014年空气质量最差,2018年空气质量最好;2013—2019年,青岛市O₃浓度总体呈上升趋势,其余5项污染物浓度呈下降趋势;青岛市环境空气主要污染物是PM_2.5,其次是O₃;青岛市空气质量冬季差于其他季节,春节期间烟花爆竹燃放等人为活动使空气质量变差。